Observed annual average changes in crucial agricultural inputs are an important communication tool (Figure 2.2). However, the coarse resolution of annual averages does not capture the higher seasonal resolutions required for on-farm management decision-making, and of more use are research concerning seasonal patterns and associated projections. Observed changes in summer rainfall since 1950 show extensive drying has occurred in the SW of WA (Whetton et al. 2005). Around half of the reduction in observed rainfall from 1958-1975 to 1976-2003 was due to a reduction in the number of troughs linked to wet conditions with the other half being associated with other synoptic types (Hope, Drosdowsky, and Nicholls 2006). Winter rainfall in the southwest has decreased substantially since 1950, and decreased abruptly in the mid 1970s by
43
around 15-20%. The largest observed decrease is from March to July, while August to October rainfall has actually increased (Australian Greenhouse Office 2007).
Figure 2.2: Trend in annual total rainfall (1950 – 2008, mm/10 years). Source:
(Bureau of Meteorology 2009).
The SW of WA is less strongly affected by ENSO than the rest of Australia (Nicholls 1991), and the rainfall decrease in the SW of WA is likely to be a combination of increased GHG concentrations, natural climate variability, and
44
land-use change (Australian Greenhouse Office 2007). The capture of rainfall and water storage is another crucial agricultural input. Rainfall flow into Perth’s dams has dropped by about 50% since the mid 1970s from the 1911-74 long-term average (Power, Sadler, and Nicholls 2005). Increasing trends in atmospheric pressure in mid winter correlate with the rainfall change in the region (Australian Greenhouse Office 2007).
The Intergovernmental Panel on Climate Change’s 2007 4AR included a large-scale global projection of relative changes in runoff by the end of the 21st century representing the median values of 12 climate models using the SRES (Special Report on Emission Scenarios) A1B scenario. In this projection, the SW of WA saw a 90% model agreement on a reduction in runoff, with the median reduction value of between 20 and 40% of 1980-1999 runoff levels (Intergovernmental Panel on Climate Change 2007). A similar multi-model projection, based on the SRESb A1B scenario, of changes of SW rainfall (for the period 2090-2099 relative to 1980-1999) was also included in the 4AR. This projection also showed a model agreement of close to 90% in a projected rainfall decrease for December to February of between 20% and 10%, and more than 90% model agreement of a decrease of between 30% and 20% for June to August (Intergovernmental Panel on Climate Change 2007).
b See section 5.1 Navigating Climate Change Economic Model Approaches for further SRES discussion.
45
In a summary of the ranges of uncertainty for rainfall changes simulated by 15 climate models for the Intergovernmental Panel on Climate Change’s 4AR, the annual average rainfall is projected to decrease by between 3% and 22% by 2030 in the extreme SW, and, 0 to 22% in the rest of the SW, and between 0% to 15%
for southern coastal regions of the SW of WA. By 2070 these rainfall change model simulation uncertainties projected annual average decreases of between 7% and 70% to occur in the extreme SW, zero to 70% in the rest of the SW, and zero to 45% for southern coastal regions (Suppiah et al. 2007). These projections may have applications for long-term farm planning, although is of little use for short-to-medium term (<30 years) farm management activities.
While noting the magnitude, spatial, and temporal uncertainties in projections such as these, climate change is expected to exacerbate current stresses on water resources from population growth and economic and land-use change, including urban areas (Intergovernmental Panel on Climate Change 2007).
Scenario information is increasingly being developed at a finer geographical resolution and been applied to the SRES storylines, producing new regional scenarios of socio-economic conditions, land use and land cover, atmospheric composition, climate and sea level (Carter et al. 2007). However, there is wide agreement between climate scientists that these projections require higher temporal and geographical resolution and verification procedures to become more reliable, although such projections are unavailable for the focus region of this research at present.
46
Climate change models are an approximation of geographically specific processes of longer-term temporal contexts (Halsaes, Kuhl, and Olesen 2007), and regional models vary in model structure, coverage, analytical approach, and assumptions (Nabuurs et al. 2007). Model validation is necessary, but not sufficient to ensure the reliability of projected changes in climate models (Suppiah et al. 2007). The use of validated simulation models developed for, or near the region of interest often incorporates systems processes that are unique to the area (Balbus et al. 1998), and the reliability of most climate models in the Australian region are tested by comparing observed and simulated precipitation, average temperature and mean sea-level pressure (Suppiah et al.
2007).
A regional modelling study by Williams et al. (2001) on the sensitivity of Australian fire danger and climate change found that a doubling of atmospheric CO2-e concentrations will increase the fire danger throughout the entire south of the continent with the equal greatest national increase of 40% in the Fire Danger Index (FDI) occurring for the southeast of WA (Williams, Karoly, and Tapper 2001). Katanning, which represented the SW in the Williams et al. (2001) study, found that the period of greatest severity occurs at the end rather than the beginning of the fire season and is more severe (with nearly half of the season is expected to have “very high” or “extreme” fire danger days and
“extreme” days doubling in frequency), whilst the length of the season remained constant (Williams, Karoly, and Tapper 2001).
47
While climate change models operate in a geographical and longer-term temporal contexts, economic analyses are based on economic decisions, or institutional boundaries within enterprises and markets, often over a much shorter timeframe (Halsaes, Kuhl, and Olesen 2007). Therefore, this research uses published peer-reviewed general agricultural production research impact findings from climate modelling to inform the assumptions that might be a crucial input to the project feasibility studies.